# Security practices designed to support enterprise and compliance requirements.

How Bralak builds: data handling, access control, AI guardrails, auditability and engineering practice. No certification is claimed.

> **No certification is claimed.** Bralak holds no security certification and does not claim one. What follows is how systems are built, and every item is something you can ask us to demonstrate on a call.

## Data handling

Where your data goes is decided before any of it moves, and written down rather than assumed.

- Data flow mapped and agreed in writing before implementation begins
- Encryption in transit and at rest across every service in the path
- Minimum necessary data: only the passages or fields required to complete the task
- Retention periods set per data category and configured, not left to default
- Where a hosted model provider is unacceptable, open models are deployed in your environment

## Access and authorisation

Permission is applied at retrieval, not at the answer — so a system cannot leak through a summary what a user could not open directly.

- Retrieval filtered by the asker’s existing entitlements
- Least-privilege service accounts, scoped per integration
- Role-based access with 2FA on every administrative surface
- Environment separation between development, preview and production
- Secrets in environment variables only — no API key ever reaches browser code
- Credential rotation, with the access scope agreed in writing beforehand

## AI guardrails

These are structural rather than configurable. A control that can be switched off under delivery pressure is not a control, so the ones that matter are properties of the design instead of settings in it.

- Actions classified by reversibility; irreversible actions require human confirmation by default
- Each tool validates its own inputs, and an agent is only given the tools it needs
- Prompt-injection defences, tested against a fixed adversarial suite
- Output constraints and validation before anything reaches a system of record
- Retrieval below a confidence threshold returns a refusal, never a generated guess
- Model choice sits behind an abstraction, so a provider can be replaced without redesign

## Auditability and observability

Behaviour is inspected after the fact rather than inferred, which is the difference between a system you can operate and one you have to trust.

- Every run traced with its inputs, decisions, tool calls and outputs
- Distributed tracing across agent handoffs, so a wrong outcome is attributable to a step
- Full audit trail on any action that writes to a system of record
- Error monitoring with alerting on failure-rate thresholds
- Evaluation suites wired into CI, so a regression blocks a release rather than being found in production

## Engineering practice

The unglamorous work that decides whether a system survives contact with volume.

- Server-side validation on every input, without exception
- Rate limiting on every public surface
- Idempotency, retry with backoff and dead-letter handling on every integration
- Dependency scanning with automated alerts
- Code review and CI gates before anything reaches production
- Architecture and data-flow review with your security team, before implementation rather than after

## Your Next Intelligent System Starts Here.

Tell us what you’re trying to improve, automate or build. We’ll help you identify the right AI strategy and engineering path.

- [Book an AI Strategy Call](https://www.bralakai.com/contact)
- [Start a Project](https://www.bralakai.com/contact)

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*Bralak AI — Building Intelligent Solutions · Automating the Future.* Bralak AI Pvt. Ltd. — Noida, UP, India.

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